A biophysical investigation on the relationships between the inputs of meal quantity percentage, carbs/sugar amount, and exercise, along with the outputs of postprandial plasma glucose, and body weight using GH-Method: Math-physical medicine (No. 295)

2020 ◽  
Keyword(s):  
Body Weight ◽  
Plasma Glucose ◽  
Influential Factors ◽  
Research Note ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
The Relationship

This research note describes the author’s investigation on differences among his three meals, which include breakfast, lunch, and dinner, in terms of their influential factors and their respective PPG data and waveforms. He further described the relationship between his body weight and meal quantity percentage for his normal portion. During this period, from 5/5/2018 to 7/14/2020, he collected detailed information of his 2,403 meals and ~64,000 glucose data.

scholarly journals Investigation the importance of metabolism on body weight and glucose for 8.5 years using GH-Method: math-physical medicine (No. 300)

2020 ◽  
Vol 5 (5) ◽  
Keyword(s):  
Time Series ◽  
Body Weight ◽  
Time Series Analysis ◽  
Plasma Glucose ◽  
Correlation Coefficients ◽  
Annual Data ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Series Analysis ◽  
Daily Data

The author uses GH-method: math-physical medicine (MPM) approach to investigate three sets of correlation between: (1) Weight vs. Metabolism Index (2) Glucose vs. Metabolism Index (3) Weight vs. Glucose - Weight is measured in early mornings and Glucose consists of daily average glucose, including both fasting plasma glucose (FPG) and three postprandial plasma glucose (PPG). He utilized time-series analysis on both his “daily data” and his “annual data” for comparison. His selected study period was 8.5 years (3,124 days) from 1/1/2012 through 7/23/2020. The reason for this specific time period was due to his weight (M1) and glucose (M2) data collection starting on 1/1/2012 along with the calculation of his metabolism index (MI) values. It is clear that, through his sophisticated math-physical medicine of metabolism and then statistical method of time-series analysis, all of these three biomarkers, weight, glucose, and metabolism index are proven to be highly correlated to each other. The following order ranking of correlation coefficients remained to be true between daily data and annual data: M1&MI > M2&MI > M1&M2 Daily: 84% > 72% > 61% Annual: 91% > 81% > 67% In other words, if you manage your metabolism (4 medical conditions and 6 lifestyle details) by controlling your disease conditions and monitoring your lifestyle details, your body weight and glucose will reduce accordingly. The author’s analyses is based on his personal biomarkers of two million data within 8.5 years (3,124 days) has further proven a simple and clean conclusion that has already been observed by many clinical physicians and healthcare professionals from their patients.

scholarly journals Investigation the importance of metabolism on body weight and glucose for 8.5 years using GH-Method: math-physical medicine (No. 300)

2020 ◽  
Vol 5 (5) ◽  
Keyword(s):  
Time Series ◽  
Body Weight ◽  
Time Series Analysis ◽  
Plasma Glucose ◽  
Correlation Coefficients ◽  
Annual Data ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Series Analysis ◽  
Daily Data

The author uses GH-method: math-physical medicine (MPM) approach to investigate three sets of correlation between: (1) Weight vs. Metabolism Index (2) Glucose vs. Metabolism Index (3) Weight vs. Glucose - Weight is measured in early mornings and Glucose consists of daily average glucose, including both fasting plasma glucose (FPG) and three postprandial plasma glucose (PPG). He utilized time-series analysis on both his “daily data” and his “annual data” for comparison. His selected study period was 8.5 years (3,124 days) from 1/1/2012 through 7/23/2020. The reason for this specific time period was due to his weight (M1) and glucose (M2) data collection starting on 1/1/2012 along with the calculation of his metabolism index (MI) values. It is clear that, through his sophisticated math-physical medicine of metabolism and then statistical method of time-series analysis, all of these three biomarkers, weight, glucose, and metabolism index are proven to be highly correlated to each other. The following order ranking of correlation coefficients remained to be true between daily data and annual data: M1&MI > M2&MI > M1&M2 Daily: 84% > 72% > 61% Annual: 91% > 81% > 67% In other words, if you manage your metabolism (4 medical conditions and 6 lifestyle details) by controlling your disease conditions and monitoring your lifestyle details, your body weight and glucose will reduce accordingly. The author’s analyses is based on his personal biomarkers of two million data within 8.5 years (3,124 days) has further proven a simple and clean conclusion that has already been observed by many clinical physicians and healthcare professionals from their patients.

Investigation the importance of metabolism on body weight and glucose for 8.5 years using GH-Method: math-physical medicine (No. 300)

2020 ◽  
Keyword(s):  
Time Series ◽  
Body Weight ◽  
Time Series Analysis ◽  
Plasma Glucose ◽  
Correlation Coefficients ◽  
Annual Data ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Series Analysis ◽  
Daily Data

The author uses GH-method: math-physical medicine (MPM) approach to investigate three sets of correlation between: Weight vs. Metabolism Index Glucose vs. Metabolism Index Weight vs. Glucose – Weight is measured in early mornings and Glucose consists of daily average glucose, including both fasting plasma glucose (FPG) and three postprandial plasma glucose (PPG). He utilized time-series analysis on both his “daily data” and his “annual data” for comparison. His selected study period was 8.5 years (3,124 days) from 1/1/2012 through 7/23/2020. The reason for this specific time period was due to his weight (M1) and glucose (M2) data collection starting on 1/1/2012 along with the calculation of his metabolism index (MI) values. It is clear that, through his sophisticated math-physical medicine of metabolism and then statistical method of time-series analysis, all of these three biomarkers, weight, glucose, and metabolism index are proven to be highly correlated to each other. The following order ranking of correlation coefficients remained to be true between daily data and annual data: M1&MI > M2&MI > M1&M2 Daily : 84% > 72% > 61% Annual : 91% > 81% > 67% In other words, if you manage your metabolism (4 medical conditions and 6 lifestyle details) by controlling your disease conditions and monitoring your lifestyle details, your body weight and glucose will reduce accordingly. The author’s analyses is based on his personal biomarkers of two million data within 8.5 years (3,124 days) has further proven a simple and clean conclusion that has already been observed by many clinical physicians and healthcare professionals from their patients.

scholarly journals Investigation the Importance of Metabolism on Body Weight and Glucose for 8.5 Years using GH-Method:Math-Physical Medicine (No. 300)

2020 ◽  
pp. 1-6
Keyword(s):  
Time Series ◽  
Body Weight ◽  
Time Series Analysis ◽  
Plasma Glucose ◽  
Correlation Coefficients ◽  
Annual Data ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Series Analysis ◽  
Daily Data

The author uses GH-method: math-physical medicine (MPM) approach to investigate three sets of correlation between: (1) Weight vs. Metabolism Index (2) Glucose vs. Metabolism Index (3) Weight vs. Glucose - Weight is measured in early mornings and Glucose consists of daily average glucose, including both fasting plasma glucose (FPG) and three postprandial plasma glucose (PPG). He utilized time-series analysis on both his “daily data” and his “annual data” for comparison. His selected study period was 8.5 years (3,124 days) from 1/1/2012 through 7/23/2020. The reason for this specific time period was due to his weight (M1) and glucose (M2) data collection starting on 1/1/2012 along with the calculation of his metabolism index (MI) values. It is clear that, through his sophisticated math-physical medicine of metabolism and then statistical method of time-series analysis, all of these three biomarkers, weight, glucose, and metabolism index are proven to be highly correlated to each other. The following order ranking of correlation coefficients remained to be true between daily data and annual data: M1&MI > M2&MI > M1&M2 Daily: 84% > 72% > 61% Annual: 91% > 81% > 67% In other words, if you manage your metabolism (4 medical conditions and 6 lifestyle details) by controlling your disease conditions and monitoring your lifestyle details, your body weight and glucose will reduce accordingly. The author’s analyses is based on his personal biomarkers of two million data within 8.5 years (3,124 days) has further proven a simple and clean conclusion that has already been observed by many clinical physicians and healthcare professionals from their patients.

scholarly journals Investigation the Importance of Metabolism on Body Weight and Glucose for 8.5 Years using GH-Method: Math-Physical Medicine (No. 300)

2020 ◽  
pp. 1-6
Keyword(s):  
Time Series ◽  
Body Weight ◽  
Time Series Analysis ◽  
Plasma Glucose ◽  
Correlation Coefficients ◽  
Annual Data ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Series Analysis ◽  
Daily Data

The author uses GH-method: math-physical medicine (MPM) approach to investigate three sets of correlation between: (1) Weight vs. Metabolism Index (2) Glucose vs. Metabolism Index (3) Weight vs. Glucose - Weight is measured in early mornings and Glucose consists of daily average glucose, including both fasting plasma glucose (FPG) and three postprandial plasma glucose (PPG). He utilized time-series analysis on both his “daily data” and his “annual data” for comparison. His selected study period was 8.5 years (3,124 days) from 1/1/2012 through 7/23/2020. The reason for this specific time period was due to his weight (M1) and glucose (M2) data collection starting on 1/1/2012 along with the calculation of his metabolism index (MI) values. It is clear that, through his sophisticated math-physical medicine of metabolism and then statistical method of time-series analysis, all of these three biomarkers, weight, glucose, and metabolism index are proven to be highly correlated to each other. The following order ranking of correlation coefficients remained to be true between daily data and annual data: M1&MI > M2&MI > M1&M2 Daily: 84% > 72% > 61% Annual: 91% > 81% > 67% In other words, if you manage your metabolism (4 medical conditions and 6 lifestyle details) by controlling your disease conditions and monitoring your lifestyle details, your body weight and glucose will reduce accordingly. The author’s analyses is based on his personal biomarkers of two million data within 8.5 years (3,124 days) has further proven a simple and clean conclusion that has already been observed by many clinical physicians and healthcare professionals from their patients.

scholarly journals Investigating the Influential Factors on Body Weight and Its Impact on Glucoses Using GH-Method: Math-Physical Medicine (No. 327)

2020 ◽  
pp. 1-3
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Body Weight ◽  
Correlation Coefficient ◽  
Pearson Correlation ◽  
Influential Factors ◽  
Pearson Correlation Coefficient ◽  
Physical Medicine ◽  
Degree Of Association

The author attempts to identify three major and two secondary influential factors of body weight along with its impact on six different glucose components using Pearson correlation coefficient “R” of statistics to calculate different degree of association between two datasets. This investigation utilized the daily weight and glucose data in conjunction with six lifestyle details, including food, exercise, water, sleep, and weather temperature, during a period of ~6 years from 1/1/2015 to 9/11/2020

scholarly journals Coefficient of GH.F-Modulus in the Linear Elastic Fasting Plasma Glucose Behavior Study Based on Health Data of Three Diabetes Patients Using the GH-Method: Math-Physical Medicine, Part 4 (No. 356)

2020 ◽  
pp. 1-7
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Health Data ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Linear Elastic ◽  
Fasting Plasma ◽  
Behavior Study ◽  
Diabetes Patients

This article is Part 4 of the author’s linear elastic glucose behavior study, which focuses on fasting plasma glucose (FPG) component. It is the continuation of his previous three studies, Parts 1, 2, and 3, on linear elastic postprandial plasma glucose (PPG) behaviors

scholarly journals Accuracy of Predicted Glucose using both Natural Intelligence and Artificial Intelligence via GH-Method: Math-Physical Medicine (No. 320)

2021 ◽  
pp. 1-4
Author(s):  
Gerald C Hsu ◽  
Keyword(s):  
Artificial Intelligence ◽  
Plasma Glucose ◽  
Fasting Plasma Glucose ◽  
Physical Medicine ◽  
Postprandial Plasma Glucose ◽  
Daily Average ◽  
Fasting Plasma ◽  
Average Glucose

This paper describes the accuracy of using natural intelligence (NI) and artificial intelligence (AI) methods to predict three glucoses, fasting plasma glucose (FPG), postprandial plasma glucose (PPG), and daily average glucose, in comparison with the actual measured PPG by using the finger-piercing (Finger) method. The entire glucose database contains 7,652 glucoses (4 glucose data per day) over 1,913 days from 6/1/2015 through 8/27/2020

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